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After some reading on- and off-line, I'm exploring the use of -clogit- to
counter this (although I incur a substantial df cost since -clogit- models
only produce estimates for groups and not individuals). As a test, I've
fitted this model:

note: multiple positive outcomes within groups encountered.
note: 66 groups (263 obs) dropped due to all positive or all negative
      outcomes.

[...]

Conditional (fixed-effects) logistic regression   Number of obs =       1249
                                                  Wald chi2(12) =     175.60
                                                  Prob > chi2   =     0.0000
Log pseudolikelihood = -374.99176                 Pseudo R2     =     0.3333

                       (standard errors adjusted for clustering on newparln)
----------------------------------------------------------------------------
           |               Robust
   con01ge |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------+----------------------------------------------------------------
   con99le |   1.965858   .2322615     8.46   0.000     1.510634    2.421082
   lab99le |  -1.366049   .3604063    -3.79   0.000    -2.072432   -.6596655
   ldm99le |  -.2783222   .2425517    -1.15   0.251    -.7537148    .1970703
   con00le |   2.268708   .3634956     6.24   0.000      1.55627    2.981147
   lab00le |  -.6086171   .5684727    -1.07   0.284    -1.722803    .5055689
   ldm00le |   .0275779   .3858629     0.07   0.943    -.7286996    .7838553
  toutle99 |   .0192292   .0396104     0.49   0.627    -.0584058    .0968643
  tout00le |  -.1089908   .0429779    -2.54   0.011    -.1932259   -.0247558
  gecpst01 |   .4763015   .1203856     3.96   0.000       .24035    .7122531
  gecxpc01 |   .1244878   .0745309     1.67   0.095    -.0215901    .2705657
  ghinps01 |   .1295617   .0822547     1.58   0.115    -.0316546     .290778
  ghinxp01 |   .0834748   .0889073     0.94   0.348    -.0907802    .2577299
----------------------------------------------------------------------------

All of these variables are extremely well-behaved as far as collinearity
are concerned. The main findings here are that voting Conservative at
local council elections 1 or 2 years earlier before the general election
(GE)raised the odds of voting Conservative again at the GE, as we would
expect, net of all other effects.

The key question is this: are these valid findings from fitting such a
model? To ask a 'supplementary' question: if using -clogit- _is_ valid, am
I right in saying that including 'alternative-specfic constants' (Liao,
1994: 61-2) wouldn't make sense in estimating the above model? (it
certainly produces garbage output!). Thanks.

CLIVE NICHOLAS        |t: 0(044)7903 397793
Politics              |e: clive.nicholas@ncl.ac.uk
Newcastle University  |http://www.ncl.ac.uk/geps

Liao TF (1994) INTERPRETING PROBABILITY MODELS, Sage QASS Series Paper
07-101, Thousand Oaks, CA: Sage.
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